Rate My Professor Xuan Wang

Xuan Wang is an Assistant Professor in the Division of Biostatistics within the Department of Population Health Sciences at the Spencer Fox Eccles School of Medicine, University of Utah. She earned her Bachelor of Science degree from Beijing Jiaotong University, her PhD from the Academy of Mathematics and Systems Science of the Chinese Academy of Sciences, and held postdoctoral fellowships at the University of Washington and Harvard University. These experiences laid the groundwork for her expertise in advanced statistical methodologies. Her research specializes in statistical methods for surrogate validation, causal inference and missing data analysis, complex survival data analysis, supervised learning, semi-supervised learning, and federated transfer learning. Wang applies these novel approaches to real-world data, particularly electronic health records (EHR), to improve risk prediction, model transportability, and surrogate evaluation in clinical contexts.

Wang has contributed extensively to biostatistics and biomedical informatics through peer-reviewed publications in top journals including JAMA Cardiology, Biometrics, Biometrika, Journal of Biomedical Informatics, and Statistics in Medicine. Key publications encompass 'Quantifying and Interpreting the Prediction Accuracy of Models for the Time of a Cardiovascular Event—Moving Beyond C Statistic: A Review' (JAMA Cardiology, 2023), 'Risk prediction with imperfect survival outcome information from electronic health records' (Biometrics, 2023), 'SurvMaximin: Robust federated approach to transporting survival risk prediction models' (Journal of Biomedical Informatics, 2022), 'Robust approach to combining multiple markers to improve surrogacy' (Biometrics, 2023), 'Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker' (Biometrika, 2020), 'Endovascular aneurysm repair devices as a use case for postmarketing surveillance of medical devices' (JAMA Internal Medicine, 2023), and 'Semiparametric joint modeling to estimate the treatment effect on a longitudinal surrogate with application to chronic kidney disease trials' (Biometrics, 2025). With 73 publications and over 1,000 citations on ResearchGate, her work enhances clinical research by providing robust tools for prediction accuracy and treatment effect assessment using imperfect data sources.